Further Validation of Artificial Neural Network-Based Emissions Simulation Models for Conventional and Hybrid Electric Vehicles

Abstract

With the advent of hybrid electric vehicles, computerbased vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavyduty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO 2 ] and oxides of nitrogen [NO x ]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO 2 and NO x in the case of a class-8 truck but were more accurate as the truck weight increased.

Prediction of NOx Vehicular Emissions Using On-Board Measurement and Chassis Dynamometer Testing

“…Oduro et al [6] proposed multiple regression models with instantaneous speed and acceleration as a predictor variables to estimate vehicular emissions of CO 2 but not NO X . Tóth-Nagy et al [7] proposed an artificial neural networkbased model for predicting emissions of CO and NO X from heavy-duty diesel conventional and hybrid vehicles. The methodology sounds promising, but applied to heavyduty vehicles only, and the fit function contains many details which make the model difficult to understand.…”

An intelligent algorithm for accurate forecasting of short term solid waste generation

“…Training algorithm in ANNs tries to model the complex relationship between independent variables (input to the network) and dependent variables (output of network) if there is sufficient data provided. Therefore, to model complex problems in economic, finance, medical diagnosis ANN models are widely used (Toth-Nagy et al, 2006). Application of ANNs in waste management has pervaded recently, prediction the rate of leachate flow in solid waste in Istanbul, Turkey (Karaca & Özkaya, 2006); utilizing multilayer perceptron neural networks to estimate energy content of Taiwan MSW (Shu et al, 2006); examining the characteristics of Hydrogen chloride (HCl) emitted from coal co-fired fluidized beds using back propagation neural networks (Chi and ZHANG, 2005); use of models based on ANN to assess recycling capacity and recycling strategy (Liu et al, 2002); and estimation of heat generation from urban solid waste using ANN and multi-variable linear regression in Nanjing, China.…”

Investigation of Emissions and Fuel Economy for the Integrated Bus Information System

“…Neural network based models usually required continuous data as inputs such as engine speed, torque, derivatives of engine speed and torque over time, axle speed, coolant temperature, exhaust temperature, oil temperature, intake air temperature, in-cylinder pressure derived variables, air mass, fuel mass, etc. [35][36][37][38][39][40][41][42][43][44][45][46][47] and could predict second-by-second emissions or fuel consumption rates.…”

“…Results showed good agreement. The model was further validated using additional driving cycles [44].…”